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The Metabolomics Analysis skill empowers researchers and data scientists to perform sophisticated metabolic profiling by providing structured guidance on metabolite identification, pathway enrichment, and flux index calculations. It assists in interpreting complex biochemical patterns—such as substrate depletion, enzymatic bottlenecks, and pathway shifts—and offers ready-to-use Python snippets for calculating critical metrics like Adenylate Energy Charge (AEC) and redox states. By integrating domain-specific logic and literature search strategies, this skill helps transform raw metabolic data into meaningful biological insights while helping users avoid common pitfalls like compartmentalization errors or isomer confusion.
